{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d1f20875-c50d-46df-906b-49ec1d0c6002",
   "metadata": {},
   "source": [
    "# 6.6 使用多元逻辑回归实现鸢尾花分类"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c90a18b9-d8e8-431c-856c-35f1e4ed05e1",
   "metadata": {},
   "source": [
    "### 1.任务描述\n",
    "\n",
    "要求：\n",
    "\n",
    "- 从sklearn.datasets模块中获取鸢尾花数据集\n",
    "- 对数据集进行部分取样，抽取出山鸢尾和变色鸢尾样本中的花萼长和花萼宽两个特征\n",
    "- 通过预处理，将采样数据转换成可训练数据\n",
    "- 搭建二元逻辑回归模型。\n",
    "- 训练模型，绘制模型曲线"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f5b4fc39-cbcf-432a-bf1e-e75e642d4b87",
   "metadata": {},
   "source": [
    "### 2.知识准备\n",
    "\n",
    "见教程。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b624ebee-980f-4c1e-b963-a24ff0b669f6",
   "metadata": {},
   "source": [
    "### 3.任务分析\n",
    "\n",
    "为了分类结果的可视化结果更直观，只取用其中两种特征来进行模型训练。通过切片法取出其中两列特征数据后，对这两列特征数据进行中心化，使其以零点为中心分布，至此就完成了训练数据的预处理。\n",
    "\n",
    "由于只涉及两种鸢尾花的分类，所以可以使用Sigmoid激活函数来进行模型训练。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435c6090-cfda-4f46-a550-22a368e41e4a",
   "metadata": {},
   "source": [
    "### 4.任务实施\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ec75eb6c-5da3-467d-a471-ca3b47242dd6",
   "metadata": {},
   "source": [
    "执行代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2ae9da58-e339-4d22-9f8d-ca255711d89e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 3) (100, 1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import tensorflow as tf\n",
    "from sklearn import datasets\n",
    "# 处理中文乱码\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "# 1，从datasets中获取数据集\n",
    "# 1.1，获取训练集\n",
    "iris = datasets.load_iris().data\n",
    "# 1.2，获取训练数据特征（花萼长和花萼宽）\n",
    "train_x = iris[:,0:2]\n",
    "# 1.3，从datasets中获取鸢尾花标签列\n",
    "train_y = datasets.load_iris().target\n",
    "\n",
    "# 1.4，获取山鸢尾和变色鸢尾的样本特征\n",
    "x_train=train_x[train_y<2]\n",
    "\n",
    "# 1.5，获取山鸢尾和变色鸢尾的样本标签\n",
    "y_train=train_y[train_y<2]\n",
    "\n",
    "# 2，数据预处理\n",
    "# 2.1，数据归一化\n",
    "# 由于花萼长和花萼宽的尺度相同，所以不用进行归一化\n",
    "\n",
    "# 2.2，数据中心化\n",
    "# 需要按列中心化，指定axis=0\n",
    "x_train=x_train-np.mean(x_train,axis=0)\n",
    "\n",
    "# 2.3，数据形状转换\n",
    "x0_train=np.ones(len(x_train)).reshape(-1,1)\n",
    "# 形状为78*3\n",
    "X=tf.concat((x0_train,x_train),axis=1)\n",
    "# print(X)\n",
    "X=tf.cast(X,tf.float32)\n",
    "# 形状为78*1\n",
    "Y=y_train.reshape(-1,1)\n",
    "Y=tf.cast(Y,tf.float32)\n",
    "print(X.shape,Y.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "adde29a2-df7f-430f-be41-5c492a6cf674",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tf.Variable 'Variable:0' shape=(3, 1) dtype=float32, numpy=\n",
      "array([[-0.01337706],\n",
      "       [-1.1628988 ],\n",
      "       [-0.22487308]], dtype=float32)>\n",
      "30 0.4885017 0.98\n",
      "60 0.31884614 0.99\n",
      "90 0.24410412 1.0\n",
      "120 0.20173997 1.0\n",
      "[[ 0.04691981]\n",
      " [ 2.131389  ]\n",
      " [-2.2534163 ]]\n"
     ]
    }
   ],
   "source": [
    "# 3，设置超参数\n",
    "# 学习率\n",
    "lr=0.2\n",
    "# 迭代次数\n",
    "iter=120\n",
    "# 显示频率\n",
    "display_step=30\n",
    "\n",
    "# 模型参数\n",
    "np.random.seed(612)\n",
    "# 形状为3*1\n",
    "W=tf.Variable(np.random.randn(3,1),dtype=tf.float32)\n",
    "print(W)\n",
    "\n",
    "# 4，训练模型\n",
    "# 存放训练集的交叉熵损失\n",
    "ce=[]\n",
    "# 存放训练集的分类准确率\n",
    "acc=[]\n",
    "for i in range(1,iter+1):\n",
    "    with tf.GradientTape() as tape:\n",
    "        # 线性输出\n",
    "        lineout=tf.matmul(X,W)\n",
    "        # 计算预测值\n",
    "        PRED=1/(1+tf.exp(-lineout))\n",
    "        # 计算损失\n",
    "        Loss=-tf.reduce_mean(Y*tf.math.log(PRED)+(1-Y)*tf.math.log(1-PRED))\n",
    "    #计算准确率\n",
    "    accuracy=tf.reduce_mean(tf.cast(tf.equal(tf.where(PRED.numpy()<0.5,0.,1.),Y),tf.float32))\n",
    "    # 添加到列表\n",
    "    ce.append(Loss)\n",
    "    # 添加到列表\n",
    "    acc.append(accuracy)\n",
    "    # 计算导数\n",
    "    dL_dW=tape.gradient(Loss,W)\n",
    "    # 更新参数\n",
    "    W.assign_sub(lr*dL_dW)\n",
    "\n",
    "    if i % display_step==0:\n",
    "        print(i,Loss.numpy(),accuracy.numpy())\n",
    "\n",
    "    \n",
    "print(W.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ba7578f-607a-453f-9354-6be87b120587",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5，可视化结果\n",
    "# 5.1，绘制损失和准确率\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(131)\n",
    "plt.xlabel(\"迭代次数\",fontsize='large')\n",
    "plt.ylabel(\"损失\",fontsize='large')\n",
    "plt.plot(ce,color=\"blue\",label=\"Loss\")\n",
    "plt.subplot(132)\n",
    "plt.xlabel(\"迭代次数\",fontsize='large')\n",
    "plt.ylabel(\"准确率\",fontsize='large')\n",
    "plt.plot(acc,color=\"red\",label=\"acc\")\n",
    "\n",
    "# 5.2，绘制散点图\n",
    "plt.subplot(133)\n",
    "plt.xlabel(\"花萼长\",fontsize='large')\n",
    "plt.ylabel(\"花萼宽\",fontsize='large')\n",
    "y_train = [int(i) for i in y_train]\n",
    "for i in range(len(x_train[:,0])):\n",
    "    if (y_train[i] == 0):\n",
    "        plt.scatter(x_train[:,0][i],x_train[:,1][i],c='g',marker='*')\n",
    "    else:\n",
    "        plt.scatter(x_train[:,0][i],x_train[:,1][i],c='r',marker='x')\n",
    "\n",
    "# 5.3，绘制决策边界\n",
    "x_=[-1.5,1.5]\n",
    "# 使用预测到的W值计算y轴数据\n",
    "y_=-(W[1]*x_+W[0])/W[2]\n",
    "# 可视化拟合直线\n",
    "plt.plot(x_,y_,color=\"g\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f986030-a14c-49a5-89d2-3e764bc66822",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
